ENHANCED MULTISTAGE VECTOR QUANTIZATION FOR SAR RAW DATA COMPRESSION

Multistage Vector Quantization(MSVQ) can achieve very low encoding and storage complexity in comparison to unstructured vector quantization. However, the conventional MSVQ is suboptimal with respect to the overall performance measure. This paper proposes a new technology to design the decoder codebook, which is different from the encoder codebook to optimise the overall performance. The performance improvement is achieved with no effect on encoding complexity, both storage and time consuming, but a modest increase in storage complexity of decoder.     

METHOD OF VECTOR QUANTIZATION CODING USING RECTANGULAR TRANSFORM FOR IMAGE COMPRESSION

First of all a simple and practical rectangular transform is given,and then thevector quantization technique which is rapidly developing recently is introduced.We combinethe rectangular transform with vector quantization technique for image data compression.Thecombination cuts down the dimensions of vector coding.The size of the codebook can reasonablybe reduced.This method can reduce the computation complexity and pick up the vector codingprocess.Experiments using image processing system show that this method is very effective inthe field of image data compression.     

基于小波变换的空间约束矢量量化方法

图像的小波变换能同时提供空间－频率局部化信息，而且小波变换域内矢量量化数据压缩已得到广泛应用，经过小波变换后，各子带小波分量存在相关性和空间约束，同时考虑到人类视觉对水平和垂直方向高频分量比对角方向更加敏感，本文提出了基于空间约束的矢量量化方法，该算法能同时提高编码效率和改善重构图像质量。     

电子封装常用名称及术语汇集

电子封装常用名称及术语汇集下面，按英文字母顺序，汇集并解释了与目前ＬＳＩ（包括ＩＣ）正在采用的主要封装形式相关联的名称术语等。这些名称术语参考并引用了日本国内１２个半导体制造公司，其他国家７个半导体制造公司＊与ＬＳＩ封装相关的资料、日本电子机械工业会...     

采用空间矢量组合的小波图像分类矢量量化

该文提出了采用空间矢量组合对小波图像进行分类矢量量化的新方法。该方法充分利用了各高频子带系数的频率相关性和空间约束性将子带系数重组，依据组合矢量能量和零树矢量综合判定进行分类，整幅图像只需单一量化码书，分类信息占用比特数少，并采用了基于人眼视觉特性的加权均方误差准则进行矢量量化，提高了量化增益。仿真结果表明，该方法实现简单，在较低的编码率下，可达到很好的压缩效果。     

Design, performance, and complexity analysis of residualtrellis-coded vector quantizers

Multistage trellis-coded vector quantization (MS-TCVQ) is developed as a constrained trellis source-coding technique. The performance of the two-stage TCVQ is studied for Gaussian sources. Issues of stage-by-stage design, output alphabet selection, and complexity are addressed with emphasis on selecting and partitioning the stage codebooks. For a given rate, MS-TCVQ achieves low encoding and storage complexity compared to TCVQ, and comparisons with same-dimensional multistage vector quantization indicate a 0.5-3-dB improvement in signal-to-quantization-noise ratio     

Multiple shell structured hypercube feature maps for vector quantization of images

A new neural network architecture is proposed for spatial domain image vector quantization (VQ). The proposed model has a multiple shell structure consisting of binary hypercube feature maps of various dimensions, which are extended forms of Kohonen's self-organizing feature maps (SOFMs). It is trained so that each shell can contain similar-feature vectors. A partial search scheme using the neighborhood relationship of hypercube feature maps can reduce the computational complexity drastically with marginal coding efficiency degradation. This feature is especially proper for vector quantization of a large block or high dimension. The proposed scheme can also provide edge preserving VQ by increasing the number of shells, because shells far from the origin are trained to contain edge block features.     

Distributed video coding based on vector quantization: Application to capsule endoscopy

We present in this paper a new distributed video coding (DVC) architecture for wireless capsule endoscopy. It is based on the state of the art DVC systems, but without using key frames. Instead, it uses an adapted vector quantization (VQ) with a searching complexity that is shifted to the decoder. VQ allows creating a good side information (SI) by exploiting the similarities in human anatomy. Thus, SI is created from a codebook (CB) rather than by motion compensated prediction. This approach decreases largely the complexity of the encoder, which codes only Wyner-Ziv frames, and allows a progressive decoding. The encoder of the proposed DVC generates only a simple hash that is used by the decoder to select the corresponding VQ codeword. The obtained experimental results show that rate-distortion results are better than those of JPEG, and show the possibility of using scalable coding to control the used rate and energy.     

Fast codebook searching in a SOM-based vector quantizer for image compression

We propose a novel method for fast codebook searching in self-organizing map (SOM)-generated codebooks. This method performs a non-exhaustive search of the codebook to find a good match for an input vector. While performing an exhaustive search in a large codebook with high dimensional vectors, the encoder faces a significant computational barrier. Due to its topology preservation property, SOM holds a good promise of being utilized for fast codebook searching. This aspect of SOM remained largely unexploited till date. In this paper we first develop two separate strategies for fast codebook searching by exploiting the properties of SOM and then combine these strategies to develop the proposed method for improved overall performance. Though the method is general enough to be applied for any kind of signal domain, in the present paper we demonstrate its efficacy with spatial vector quantization of gray-scale images.     

归一化自适应预测矢量量化算法压缩SAR原始数据

该文提出归一化自适应预测矢量量化(NAPVQ)算法压缩SAR原始数据。NAPVQ算法先采用矢量线性预测器对输入矢量进行预测,再对原矢量与预测矢量之间的残差矢量进行矢量量化。该算法可视为差分脉冲调制在矢量量化中的拓展,其性能优于块自适应量化(BAVQ)算法以及归一化预测自适应量化(NPAQ)算法。对算法复杂度的进一步分析表明,NAPVQ算法能获得复杂度和性能之间比较合理的折衷,具有实用价值。     

On the structure of vector quantizers

Vector quantization is intrinsically superior to predictive coding, transform coding, and other suboptimal and {em ad hoc} procedures since it achieves optimal rate distortion performance subject only to a constraint on memory or block length of the observable signal segment being encoded. The key limitation of existing techniques is the very large randomly generated code books which must be stored, and the computational complexity of the associated encoding procedures. The quantization operation is decomposed into its rudimentary structural components. This leads to a simple and elegant approach to derive analytical properties of optimal quantizers. Some useful properties of quantizers and algorithmic approaches are given, which are relevant to the complexity of both storage and processing in the encoding operation. Highly disordered quantizers, which have been designed using a clustering algorithm, are considered. Finally, lattice quantizers are examined which circumvent the need for a code book by using a highly structured code based on lattices. The code vectors are algorithmically generated in a simple manner rather than stored in a code book, and fast algorithms perform the encoding algorithm with negligible complexity.     

量化方法及其统计特征量用于图像检索的性能比较

分别对标量量化,矢量量化以及分类矢量量化等不同量化方法及其统计特征量用于图像检索的性能进行了分析和比较,对进一步实现支持检索的图像压缩算法具有一定的指导意义。     

一种改进的矢量量化码字搜索算法

该文利用图像矢量的平均值和方差,结合了最近邻域搜索算法,构造了一种新的快速矢量量化编码算法。将一个输入矢量分为两个子矢量,分别计算原始矢量、两个子矢量的和以及方差值,利用在这些数值基础上建立的一组三角不等式来排除不可能的码字。仿真结果表明新算法在所需时间和计算复杂度方面优于改进的EENNS算法,为矢量量化算法的研究提供了一种新的思路。     

Wavelet image coding using variable blocksize vector quantization with optimal quadtree segmentation

In this paper, we propose an image coding scheme by using the variable blocksize vector quantization (VBVQ) to compress wavelet coefficients of an image. The scheme is capable of finding an optimal quadtree segmentation of wavelet coefficients of an image for VBVQ subject to a given bit budget, such that the total distortion of quantized wavelet coefficients is minimal. From our simulation results, we can see that our proposed coding scheme has higher performance in PSNR than other wavelet/VQ or subband/VQ coding schemes.     

An improved vector quantization method using deep neural network

To address the challenging problem of vector quantization (VQ) for high dimensional vector using large coding bits, this work proposes a novel deep neural network (DNN) based VQ method. This method uses a k-means based vector quantizer as an encoder and a DNN as a decoder. The decoder is initialized by the decoder network of deep auto-encoder, fed with the codes provided by the k-means based vector quantizer, and trained to minimize the coding error of VQ system. Experiments on speech spectrogram coding demonstrate that, compared with the k-means based method and a recently introduced DNN-based method, the proposed method significantly reduces the coding error. Furthermore, in the experiments of coding multi-frame speech spectrogram, the proposed method achieves about 11% relative gain over the k-means based method in terms of segmental signal to noise ratio (SegSNR).     

一种基于小波变换和矢量量化的图像压缩算法

小波变换和矢量量化都是图像压缩中的重要方法。利用小波变换的系数特点,对图像进行小渡分解,对于能量最为集中的低频分量采用标量量化处理,然后将标量量化过程中产生的残差和高频分量一起构造矢量,进行矢量量化。实验结果表明,此算法能够有效提高重构图像质量,获得较高的信噪比。     

IMPLEMENTING THE ADAPTIVE VECTOR QUANTIZER USING CARPENTER/GROSSBERG NET

AVQ(Adaptive Vector Quantizer)overcomes some shortcomings of traditional vectorquantizer with a fixed codebook trained and generated by the LBG or other algorithms by applyinga variab|e codebook.In this paper,we describe an effective and efficient implementation of AVQby modifying the CCN(Carpenter/Grossberg Net).The encoding process of AVQ is very similarto the learning process of the CGN.We study several different encoding schemes,includingwaveform AVQ,analysed parameter AVQ and so on,implemented by the CGN.And we simulatethe encoding performance of each scheme for encoding Gaussian process source,first order Gauss-Markov process source and practical speech signal.Our simulation results show that good qualityboth in subjective and objective tests can be obtained in a low or middle bit rate range.     

结合矢量量化的SPIHT算法用于多光谱图像压缩

针对多波段遥感图像纹理复杂丰富、局部相关性较弱的特点,提出了结合矢量量化的SPIHT压缩算法。将经过小波变换后的遥感图像谱间相同位置的系数聚集构成矢量,根据高频子图的局部块纹理强弱进行自适应性的量化。使基于标量的SPIHT算法能够方便的处理矢量,有效去除数据间各类相关。实验表明,该方法对多波段遥感图像的压缩可以收到良好的效果,且算法具有良好的实时性,对单幅图像的压缩比和峰值信噪比(PSNR)均优于普通的二维SPIHT算法。     

矢量量化在语音波形编码中的应用

矢量量化(VQ)技术是近几年发展起来的一种高效数据压缩技术.本文介绍了VQ技术的发展历史、现状和它的基本原理,较为详细地讨论了基本矢量量化器的实用设计方法——LBG算法,并对原有的LBG算法进行了改进,给出了实验结果.     

基于记忆和预测机制的自适应矢量量化及其在图像压缩编码中的应用

本文提出了一种用于图像压缩的低比特率自适应矢量量化技术，该方法有以下三个特色：１）引入了有效的记忆预测机制，使量化器和编码器有很好的自适应性。２）采用二次寻址方法对矢量的地址进行编码，大大提高了编码效率；３）算法运算复杂度低，便于ＶＬＳＩ实现。